However, DNNs are very prone to over-fitting and the available dataset is relatively small. For that reason we propose using CNNs since these are more robust against over-fitting – note that CNNs allow parameter sharing. Additionally, Pons et al. [1]} have successfully used spectrograms-based CNNs for learning music timbre representations from small datasets. Given that timbre is an important feature for acoustic modeling, we propose using the same architecture: a single convolutional layer with filters of various sizes [1]}, [3]}. The input is set to be a log-mel spectrogram. We use 128 filters of sizes \(50{\times }1\)  and \(70{\times }1\) , 64 filters of sizes \(50{\times }5\)  and \(70{\times }5\) , and 32 filters of sizes \(50{\times }10\)  and \(70{\times }10\)  – where the first and second numbers denote the frequential and temporal size of the filter, respectively. A max-pool layer of \(2{\times }N^{\prime }\)  is followed by a 32-way softmax output layer with 30% dropout – where \(N^{\prime }\)  denotes the temporal dimension of the resulting feature map. \(2{\times }N^{\prime }\)  max-pool layer was chosen to achieve time-invariant representations while keeping the frequency resolution. And same padding is used to preserve the dimensions of the feature maps so that these are concatenable.
Filter shapes are designed so that filters can capture the relevant time-frequency contexts for learning timbre representations – according to the design strategy proposed by Pons et al. [1]}
